code: def get_lm():
      n_layers = 1
      n_units = 4
  
      char_list = ["<blank>", "<space>", "a", "b", "c", "d", "<eos>"]
  
      rnnlm = lm_pytorch.ClassifierWithState(
          lm_pytorch.RNNLM(
              len(char_list), n_layers, n_units, typ="lstm"
          )
      )
  
      return rnnlm
  
imports: import argparse
  import json
  import tempfile
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.transducer.blocks import build_blocks
label_desc: Use image node_id 4 for calling a global function with example usage: get_lm() and returns: rnnlm